Constraining scalar-tensor theories using neutron star mass and radius measurements

TL;DR

This paper explores how neutron star mass and radius data can constrain scalar-tensor theories of gravity, highlighting the potential and limitations of current measurements for testing fundamental physics.

Contribution

It introduces a Bayesian framework to constrain scalar-tensor theories using neutron star data, emphasizing the differing impacts of massless and massive scalars.

Findings

Massless scalars are constrained, but current bounds are weaker than binary observations.

Neutron star data effectively tests gravity for massless scalars, with potential for improvement.

Massive scalars are less constrained due to high-likelihood regions with small deviations.

Abstract

We use neutron star mass and radius measurements to constrain the spontaneous scalarization phenomenon in scalar-tensor theories using Bayesian analysis. Neutron star structures in this scenario can be significantly different from the case of general relativity, which can be used to constrain the theory parameters. We utilize this idea to obtain lower bounds on the coupling parameter $\beta$ for the case of massless scalars. These constraints are currently weaker than the ones coming from binary observations, and they have relatively low precision due to the approximations in our method. Nevertheless, our results clearly demonstrate the power of the mass-radius data in testing gravity, and can be further improved with future observations. The picture is different for massive scalars, for which the same data is considerably less effective in constraining the theory parameters in an unexpected manner. We identify the main reason for this to be a large high-likelihood region in the parameter space where deviations from general relativity are relatively small. We hope this initial study to be an invitation to use neutron star structure measurements more commonly to test alternative theories in general.